By: Eric Siegel, Founder, Predictive Analytics World
In anticipation of his upcoming conference presentation, Predicting Behavior in Chemical Industry Supply Chains, at Predictive Analytics World New York, October 23-27, 2016, we asked Gary Neights, Senior Director at Elemica, a few questions about his work in 
predictive analytics.
Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?
A: Predictive models for pharma and retail are often used to influence consumer behavior or give direction to research efforts. These models yield results that are reviewed by experts before being acted upon. The predictive system for manufacturing supply chains that I will discuss drive real-time manufacturing execution decisions by front-line employees. Commitment of resources such as labor, manufacturing capacity, raw materials, and logistics capacity can occur in near real-time. Accurate, real-time data flows from customers, distributors, suppliers, and carriers are required to develop a full picture of the situation and help provide the best level of decision support.
Q: In your work with predictive analytics, what behavior do your models predict?
A: One example is under supply or over supply conditions. Over supplying finished goods may lead to price discounting while under supplying material to a downstream manufacturing process may shut down operations. Another example is predicting which perishable materials in a complex supply chain network are nearing expiration so they can be expedited to an appropriate manufacturing facility.
Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?
A: Supply chain decisions have financial impact and need to be taken in near real-time. Product-by-product and plant-by-plant predictions can lead to information overload and indecision. For example, if rail cars to a manufacturing site are predicted to be late do I dispatch trucks as a rush shipments… or dip into safety stock? If trucks, how many? Over the long-term data may be analyzed systematically and accounted for during periodic planning cycles or contract renegotiations.
Q: Can you describe a successful result, such as the predictive lift (or accuracy) of your model or the ROI of an analytics initiative?
A: A predictive Railcar KanBan system drove a working capital savings of greater than $400K / year for one product and inventory replenishment accuracy was increased from less than 55% accuracy to greater than 80%. This allowed a 20% reduction in safety stock levels and 40% reduction of leased railcars.
Q: What surprising discovery have you unearthed in your data?
A: In one case a graphical review of time-series data indicated that a manual supply chain management process was systematically driving costly inventory swings. The planner was not correctly accounting for transit times between locations, nor the operating hours for shipping and receiving operations. This was corrected by a correctly tuned predictive system.
Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.
A: A common theme I hear is that the farther you are from the consumer the harder it is to get accurate demand data. We will share one approach that supports manufacturers systematically aggregating demand to improve predictive accuracy.
———————
Don't miss Gary’s conference presentation, Predicting Behavior in Chemical Industry Supply Chains, and workshops at Predictive Analytics World New York on Tuesday, October 25, 2016 from 3:05 to 3:25 pm. Click here to register for attendance.
By: Eric Siegel, Founder, Predictive Analytics World